Provenance Research Issues and Challenges in the Big Data Era

Cuzzocrea, Alfredo

doi:10.1109/compsac.2015.345

Cited by 10 publications

(8 citation statements)

References 24 publications

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“…综上所述, 当前大数据世系的研究虽然取得了一些成果, 但仍有很多问题有待进一步研究, 具体体现在 [290,316,317] : (1) 大数据的异构特性导致对世系数据进行统一建模和结构化描述更加困难; 不同世系系统产生的世系数据间的交互融合面临多种世系格式的兼容性问题; (2) 大数据的海量特性使得世系数据的采集、传输和存储加重了大数据平台在计算、通信和存储方面的负担, 一些研究人员提出采用世系压缩的方法降低存储和传输开销, 但数据的压缩和解压也带来新的计算开销 [288] ; (3) 大数据平台的封装和透明化, 增加了世系数据采集的实现难度, 尤其对流数据的世系采集、存储等研究仍处于起步阶段, 最新研究成果较少; (4) 世系数据的安全已引起研究人员关注, 但所提方法在大数据场景下的实用性有待进一步验证和提升; (5) 目前基于世系的数据安全监管研究大多只分析了世系数据用于数据监管的可行性并通过案例进行说明, 但缺乏基于世系检测数据泄露等安全威胁的自动化方法.…”

Section: 在世系数据的采集、存储、融合和查询中世系采集是研究人员的主要关注在大数据场景下世系数据本身也是一种大数据unclassified

Research progress on big data security technology

Chen¹,

Gao²,

Huilin³

et al. 2020

Sci. Sin.-Inf.

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Independence and controllability of big data security Chinese Science Bulletin 60, 427 (2015); Research progress in the complexity theory and algorithms of big-data computation SCIENTIA SINICA Informationis 46, 1255 (2016); Real-time intelligent big data processing: technology, platform, and applications SCIENCE CHINA Information Sciences 62, 082101 (2019); Research on theory and key technology of trusted computing platform security testing and evaluation SCIENCE CHINA Information Sciences 53, 434 (2010); Earth observation big data for climate change research

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Section: 在世系数据的采集、存储、融合和查询中世系采集是研究人员的主要关注在大数据场景下世系数据本身也是一种大数据unclassified

Research progress on big data security technology

Chen¹,

Gao²,

Huilin³

et al. 2020

Sci. Sin.-Inf.

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“…Data provenance is a well-known research area within database and data mining. It considers the problem of identifying the origin, the creation, as well as the propagation processes of data [4]. It may be defined as the process of detecting the lineage and the derivation of data and data objects [5].…”

Section: Background and Related Work On Big-data Provenancementioning

confidence: 99%

“…Data provenance is an essential component in a various areas such as: database management systems, workflow management systems, distributed systems, and debugging ICT systems [4]. In the case of security violations, a system administrator should be able to identify the origination of the error, in addition to its causes and impacts [6].…”

Section: Background and Related Work On Big-data Provenancementioning

confidence: 99%

Bio-inspired Think-and-Share Optimization for Big Data Provenance in Wireless Sensor Networks

Alkhalil¹,

Ramadan²,

Ahmad³

2019

IJACSA

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Big data systems are being increasingly adopted by the enterprises exploiting big data applications to manage datadriven process, practices, and systems in an enterprise wide context. Specifically, big data systems and their underlying applications empower enterprises with analytical decision making (e.g., recommender/decision support systems) to optimize organizational productivity, competitiveness, and growth. Despite these benefits, big data applications face some challenges that include but not limited to security and privacy, authenticity, and reliability of critical data that may result in propagation of false information across systems. Data provenance as an approach and enabling mechanism (to identify the origin, manage the creation, and track the propagation of information etc.) can be a solution to above mentioned challenges for data management in an enterprise context. Data provenance solution(s) can help stakeholders and enterprises to assess the quality of data along with authenticity, reliability, and trust of information on the basis of identity, reproducibility and integrity of data. Considering the wide spread adoption of big data applications and the needs for data provenance, this paper focuses on (i) analyzing state-of-the-art for holistic presentation of provenance in big-data applications (ii) proposing a bioinspired approach with underlying algorithm that exploits human thinking approach to support data provenance in Wireless Sensor Networks (WSNs). The proposed 'Think-and-Share Optimization' (TaSO) algorithms modularizes and automates data provenance in WSNs that are deployed and operated in enterprises. Evaluation of TaSO algorithm demonstrates its efficiency in terms of connectivity, closeness to the sink node, coverage, and execution time. The proposed research contextualizes bio-inspired computation to enable and optimize data provenance in WSNs. Future research aims to exploit machine learning techniques (with underlying algorithms) to automate data provenance for big data systems in networked environments.

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“…Provenance management fundamentals for electronic data can be applied to gain better control over data quality [42]. However, provenance management for big data poses several challenges [44], which will also have to be mitigated.…”

Section: Metadatamentioning

confidence: 99%

A survey on scholarly data: From big data perspective

Khan

Liu

Shakil

et al. 2017

Information Processing & Management

117

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With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research. In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems. This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.

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Provenance Research Issues and Challenges in the Big Data Era

Cited by 10 publications

References 24 publications

Research progress on big data security technology

Research progress on big data security technology

Bio-inspired Think-and-Share Optimization for Big Data Provenance in Wireless Sensor Networks

A survey on scholarly data: From big data perspective

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